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DivyaV18

YouTube MCP Server

by DivyaV18

list_channel_videos

Retrieve video listings from a specific YouTube channel by ID. Supports pagination and configurable result limits.

Instructions

Lists videos from a specified YouTube channel, ensuring results are of type 'video'.

Args: channel_id: The unique ID of the YouTube channel for which videos are to be retrieved. max_results: Maximum number of videos to return per page (default 5, max 50). page_token: Token for pagination; use nextPageToken from a previous response. part: Search resource properties to include; must be 'snippet' for this action.

Returns: Object with 'data' (video list), 'error' (if any), and 'successful' boolean.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
partNo
channel_idYes
page_tokenNo
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden for behavioral disclosure. It does reveal important traits: results are filtered to type 'video', pagination uses page_token with nextPageToken, max_results has defaults, and returns an object with data/error/successful. However, it omits details like authentication requirements or quota effects, so transparency is moderate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise, beginning with a clear summary sentence followed by an Args section and a Returns section. Every sentence adds value, and there is no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core behavior, parameter semantics, and return format, which is sufficient for a relatively simple listing tool. Since an output schema exists, the description does not need to detail the data field. It could optionally mention error scenarios, but the current coverage is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It does so effectively by explaining each parameter: channel_id as the unique channel identifier, max_results with default (5) and maximum (50), page_token for pagination referencing nextPageToken, and part with a required value of 'snippet'. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Lists videos from a specified YouTube channel, ensuring results are of type video.' The added constraint of filtering to video type distinguishes it from sibling tools like search_youtube or get_channel_activities, making the purpose specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that this tool retrieves channel-specific videos, but it does not explicitly state when to prefer this over siblings such as search_youtube or get_channel_activities. No exclusions or alternative recommendations are given, so the usage guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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